Monitoring of tool wear distribution with cutting force measurement in drilling
Author:
Affiliation:
1. Division of Mechanical Engineering, Ashikaga University
2. Department of Mechanical Engineering, Tokyo Denki University
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/15/4/15_2021jamdsm0047/_pdf
Reference12 articles.
1. Corne, R., Nath, C., El Mansori, M. and Kurfess, T., Study of spindle power data with neural network for predicting real-time tool wear/breakage during Inconel drilling, Journal of Manufacturing Systems, Vol.43, (2017) pp.287-295.
2. Dheeraj Simon, G. and Deivanathan, R., 2019, Early detection of drilling tool wear by vibration data acquisition and classification, Manufacturing Letters, Vol.21, (2019) pp.60-65.
3. Dimla Snr. D. E., Sensor signals for tool-wear monitoring in metal cutting operations - a review of methods, International Journal of Machine Tools and Manufacture, Vol.40, No.8 (2000), pp.1073-1098.
4. Ertunc, H. M. and Oysu, C., Drill wear monitoring using cutting force signals, mechatronics, Vol.14, No.5 (2004), pp.533-548.
5. Gowda, B. M. U., Ravindra, H. V., Ullas, M., Prakash, G. V. N. and Ugrasen, G., Estimation of circularity, cylindricity and surface roughness in drilling Al-Si3N4 metal matrix composites using artificial neural network, Procedia Materials Science, Vol.6 (2014), pp.1780-1787.
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